{"id":"W2154537621","doi":"10.1016/j.media.2009.10.002","title":"CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity measure; Similarity (geometry); Artificial intelligence; Measure (data warehouse); Image registration; Pattern recognition (psychology); Fiducial marker; Computer science; Computer vision; Mutual information; Noise (video); Phase (matter); Mathematics; Image (mathematics); Data mining; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001833812,0.000245766,0.0005245142,0.0003631519,0.0002116661,0.0003083139,0.001199549,0.0001162981,0.001263324],"category_scores_gemma":[0.003445409,0.0002151734,0.0003779957,0.002320952,0.0001813019,0.0007104799,0.00009609289,0.0003087808,0.00004712155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007252604,"about_ca_system_score_gemma":0.000296008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000127342,"about_ca_topic_score_gemma":0.00009179829,"domain_scores_codex":[0.996102,0.0002302368,0.0006987868,0.000708857,0.001782425,0.0004777232],"domain_scores_gemma":[0.9974576,0.0002355706,0.0002421669,0.0008661849,0.0005545357,0.0006440072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003805056,0.00146522,0.00008633489,0.00003201018,0.0005520648,0.0003169681,0.0003225931,0.000003540698,0.01358856,0.001700768,0.08084557,0.9010483],"study_design_scores_gemma":[0.007015917,0.001677371,0.001482893,0.00009717685,0.002165732,0.00009880111,0.0001446283,0.8651804,0.07700019,0.02889791,0.01488077,0.001358254],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007147395,0.00006259866,0.9775931,0.0192371,0.00005000976,0.0003712982,0.00001179661,0.0004766716,0.001482682],"genre_scores_gemma":[0.29283,0.00005115733,0.6854076,0.02069677,0.0002454433,0.00009588463,0.0003424498,0.00001799078,0.0003126862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8996901,"threshold_uncertainty_score":0.9996496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893558010175032,"score_gpt":0.3702821879122302,"score_spread":0.3413466078104799,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}